What Drive the Blockchain Adoption For Managers?

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In the realm of digital innovation, blockchain technology stands out as a revolutionary force reshaping industries and redefining trust in transactions.

As we delve into the report “What Drives the Adoption of Blockchain Technology? A Fit-Viability Perspective,” we embark on a journey to uncover the key elements driving the adoption of blockchain technology.

Let’s explore each chapter and unravel the insights that pave the way for a deeper understanding of this transformative technology.

Understanding Blockchain Technology

Blockchain technology has the potential to revolutionize the way digital transactions are stored, retrieved, and shared by providing a secure decentralized ledger of peer-to-peer transactions without the need for intermediaries. The advantages of blockchain technology include anonymity, transparency, security, traceability, and efficiency of transactions. However, despite its benefits, some industries have been hesitant to adopt blockchain due to uncertainties about its business value. This reluctance among managers, especially in finance and medical industries, presents a significant challenge that requires further exploration.

When considering the adoption of blockchain technology, managers should prioritize the benefits and value created by the technology. This includes assessing whether the technology can enhance business tasks, reduce costs, and improve operations. Additionally, the adoption of IT, including blockchain, can influence a firm’s reputation and self-worth. Understanding the complexity of technology adoption is crucial, and models like Task-Technology Fit (TTF), feasibility/readiness model, and the fit-viability model (FVM) can help evaluate factors related to technology, organization, and environment in the adoption process.

The Fit-Viability Model (FVM) provides a comprehensive framework for studying technology adoption, offering insights beyond what traditional models like TTF and feasibility/readiness models can provide. By extending the FVM to analyze blockchain adoption, researchers aim to understand managers’ intentions to adopt blockchain technology and identify critical factors influencing adoption within organizations, particularly in the medical and financial sectors. This study explores how industry type may impact the willingness to adopt blockchain technology, focusing on sectors that handle significant amounts of data and transactions that require protection and preservation.

Technical Disadvantages of Blockchain

Despite its numerous benefits, blockchain technology faces technical challenges like throughput limitations, latency issues, and high energy consumption. The report emphasizes the need to address these limitations to enhance the scalability and efficiency of blockchain networks. Overcoming these challenges is crucial for widespread adoption and integration of blockchain technology.

Models And Theories Applied to Blockchain

Various theoretical frameworks and models related to technology adoption in organizations have a specific focus on blockchain technology adoption.

These models and theories provide valuable frameworks for understanding the factors influencing technology adoption, including blockchain technology, within organizations. By integrating these models, researchers can gain insights into the complex dynamics of technology adoption processes and identify key drivers of adoption behavior among managers and organizations.

Methods applied to Blockchain
Summary characteristics of relevant theories. Source: Research

Technology-Organization-Environment (TOE) Model

The TOE model examines the direct effects of technological, organizational, and environmental factors on technology adoption within organizations. It considers how these factors interact and influence the adoption process. However, one limitation of the TOE model is the lack of consideration for the interaction effects among these factors.

Fit-Viability Model (FVM)

The Fit-Viability Model (FVM) is a hybrid model that combines elements of Task-Technology Fit (TTF) and feasibility considerations. It focuses on assessing the alignment between task characteristics and technological features, as well as the feasibility of adopting the technology within the organization. The FVM provides a comprehensive framework for evaluating technology adoption by considering factors related to technology, organization, and environment.

Task-Technology Fit (TTF) Model

The Task-Technology Fit (TTF) model emphasizes the alignment between task requirements and technological capabilities. It examines how well a technology meets the functional needs of a specific task or process. However, the TTF model may lack considerations for environmental factors and organizational readiness, which are crucial in the adoption process.

Value-Based Model (VBM)

The Value-Based Model (VBM) focuses on the rational choice of cost benefits associated with technology adoption. It considers the economic value derived from adopting a technology and how it can impact organizational performance. The VBM may not adequately address factors related to Task-Technology Fit and environmental considerations.

Innovation Diffusion Theory (IDT)

The Innovation Diffusion Theory (IDT) explores how technology features influence the diffusion process within a social system. It provides a process-oriented view of technology adoption and diffusion, highlighting the factors that affect the spread of innovations. However, the IDT may not fully account for interaction effects among different variables.

Unified Theory of Acceptance and Use of Technology (UTAUT)

The Unified Theory of Acceptance and Use of Technology (UTAUT) is an integrated model that considers various determinants and moderators in the intention-usage relationship of technology adoption. UTAUT focuses on factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, cost considerations, hedonic motivation, and habit. It aims to explain user intentions and behaviors regarding technology adoption in different contexts.

Analysis and Key Results


The study proposed hypotheses related to managers’ intention to adopt blockchain technology and critical factors influencing adoption within organizations, particularly in the medical and financial sectors. These hypotheses were formulated based on the theoretical frameworks and models discussed in the literature review section of the paper.

Measurements Used for Testing

  1. Reliability Testing:
    • Composite reliability and Cronbach’s alpha were used to assess the reliability of the measurement model.
    • The study examined whether each construct had Cronbach’s alpha values above a certain threshold to ensure acceptable reliability.
    • Composite reliability values were also assessed to determine the reliability of the measurement model.
  2. Convergent Validity Testing:
    • Average Variance Extracted (AVE) was used to assess convergent validity, indicating the amount of variance captured by the constructs.
    • Factor loadings were examined to ensure that they were above a certain threshold, indicating adequate convergent validity.
    • The study assessed the square root of AVE for each variable to ensure that it was greater than the correlated variables, indicating acceptable discriminant validity.
  3. Discriminant Validity Testing:
    • The study examined the correlation coefficients between variables to ensure that they were below a certain threshold, indicating acceptable discriminant validity.
    • A cross-loading matrix was used to compare factor loadings for each item in each dimension, ensuring sufficient discriminant validity.
  4. Partial Least Squares Structural Equation Modeling (PLS-SEM):
    • PLS-SEM was used to analyze the data, considering the inclusion of formative constructs and the relatively small sample size.
    • The analysis included assessing the coefficient of determination (R2), path coefficients, effect size (f2), and other model validity measures.

Overall, the analysis results in the study focused on testing the reliability and validity of the measurement model, ensuring that the constructs and variables used in the study were reliable and valid for examining managers’ intention to adopt blockchain technology and the factors influencing adoption within organizations. The measurements used for testing aimed to provide a robust analysis of the proposed hypotheses and research model.

Role of IT in Blockchain Adoption

Information technology plays a pivotal role in driving blockchain adoption, enabling firms to enhance their performance and create value. By leveraging IT capabilities and organizational resources, businesses can harness the full potential of blockchain technology. The report underscores the importance of aligning IT strategies with organizational goals to maximize the benefits of blockchain adoption.

Impact of Blockchain on Business Value

The integration of blockchain technology can significantly impact business value by enhancing security, efficiency, and transparency in operations. By leveraging blockchain for various applications like supply chain management, healthcare data sharing, and financial transactions, organizations can unlock new opportunities and streamline processes. The report highlights the transformative potential of blockchain in driving business value.


In conclusion, the research study integrated concepts from Task-Technology Fit (TTF), Environmental Task Fit (ETF), and Fit-Viability Model (FVM) to explain managers’ intention to adopt blockchain technology in organizations. Recognizing that managers play a crucial role as gatekeepers of new technologies, the study aimed to understand the motivations behind their adoption of technologies like blockchain.

By adopting perspectives of functional benefits and symbolic benefits to evaluate the intention to adopt blockchain technology, the study found that factors such as viability, fit, and symbolic elements like reputation positively influence the intention to adopt blockchain technology. These findings provide valuable insights for blockchain technology providers on how to introduce new and efficient technologies to replace or enhance existing processes.

The study’s results lay the groundwork for incorporating social impacts into models like TTF, ETF, and FVM to further explore the intention and attitude towards adopting new technologies. Overall, the research contributes to understanding the drivers of blockchain technology adoption within organizations and offers a foundation for future studies to delve deeper into the social aspects of technology adoption.

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